Risk Identification Technology for Power Operation Site Based on Deep Learning
When working in an electrified high-altitude environment,it is usually required that the operators wear protective measures such as safety helmets and safety ropes,and regulate their operating behavior to avoid safety accidents such as electric shock.Once a safety hazard is discovered,a warning should be issued in a timely manner.The traditional supervision method is manual inspection,supervised by on-site safety officers or surveillance cameras,which all have the problems of high labor costs and low efficiency.With the development of deep learning in the field of image processing,real-time object detection and ranging technology has been increasingly applied in the safety prevention and control of power operations.However,current detection algorithms have problems with high external interference and low detection accuracy in the power operation environment.Therefore,based on the latest YOLO v8,this article introduces the BoTNet module to optimize existing object detection algorithms,and only performs object detection in areas where there are operators,effectively reducing detection time and improving the accuracy of object detection.
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